The 5 Most Common Situations That Require Nonparametric Testing

Nonparametric testing is an essential skill that any business manager or marketer who performs statistical analysis should have. Statistics course normally teach only parametric statistics but there are many real-life data analysis situations in business that require nonparametric analysis. This article will examine the 5 most common situations that require nonparametric testing in the place of parametric analysis.

Statistical procedures are either parametric or nonparametric. Parametric statistical tests require assumptions about the population from which the samples are drawn. For example, many data analysis tools such as the t Test, Chi-Square tests, z Tests, and F tests, and many types of hypothesis tests require the underlying population to be normally distributed. Some of these also require equal variances of both populations.

Sometimes these requirements cannot be assumed. Examples of this would be if the population is highly skewed or if the underlying distribution or variances were entirely unknown.

Nonparametric tools have no assumptions regarding distribution of underlying populations or variance. Most of this are very easy to perform but they are not usually as precise as parametric methods and the Null Hypothesis usually difficult to reject when using a nonparametric method.

When To Use Nonparametric Methods

1) The most important use of nonparametric tools occurs when samples are drawn from populations that are not known to be normally distributed. Parametric methods require that all underlying populations are normally distributed. Parametric testing will produce wrong answers when samples are taken from non-normally distributed populations. Non-parametric testing is one answer for this situation.

2) Nonparametric approaches are often used as shortcut replacements for more complicated parametric analysis. You can quite often get a quick answer that requires little calculation by running a nonparametric test.

3) Nonparametric tools are often used when the data is ranked but cannot be quantified. For example, how would you quantify consumer rankings such as very satisfied, moderately satisfied, just satisfied, less than satisfied, dissatisfied?

4) Nonparametric statistics can be applied when there are a lot of outliers that might skew the results. Nonparametric statistics often evaluate medians rather than means and therefore if the data have one or two outliers, the outcome of the analysis is not affected.

5) They come in especially handy when dealing with non-numeric data, such as having customers rank products or attributes according to preference.

The most widely-used nonparametric tests are:

– The Sign Test

– Wilcoxon Signed Rank

– Wilcoxon Ranked Sum

– Mann-Whitney

– Kruskal-Wallis

– Spearman Correlation Coefficient

My blog contains articles with specific instructions on how and when to do each one of these nonparametric tests in Excel. Nonparametric methods are perhaps more useful than the classic parametric tools which require that samples are drawn from normally distributed populations. Nonparametric testing is rarely taught in statistics courses. That is too bad because nonparametric testing can often be a real lifesaver for anyone who has to analyze data on a regular basis.